Optimization method and device for distributed power supply grid connection

Abstract

The invention relates to a distributed power supply grid connection optimization method and device, and belongs to the technical field of power grid distribution. The method comprises the following steps: determining power grid configuration parameters of a target area; the power grid configuration parameters comprise a power distribution network main transformer, system impedance, line impedance, a power grid distribution transformer and a power grid load; constructing a distribution network simplified model based on the power grid configuration parameters of the target area; the simplified distribution network model at least comprises a plurality of divided nodes; determining a plurality of distributed power supplies to be accessed; substituting the simplified distribution network model and the plurality of distributed power supplies into a particle swarm algorithm for iteration, and determining an optimal solution of a target function; the objective function aims at minimizing network loss and voltage offset; determining an optimal power distribution network model based on the optimal solution of the objective function; the optimal power distribution network model comprises access nodes of a plurality of distributed power supplies. Through the mode, the network loss and the voltage deviation after the grid connection of the distributed power supply can be effectively reduced, and the grid stability and the electric energy quality after the grid connection are further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an optimization method and device for grid connection of distributed power sources. Background Art

[0003] Distributed Generation (DG), as an important part of new energy, has been rapidly popularized and applied in recent years. Distributed power sources generally refer to power sources with a voltage level of 35 kV and below that are not directly connected to the centralized power transmission system. They are usually close to the load center, such as residential areas, commercial areas or industrial areas, which greatly reduces the power loss during long-distance power transmission and also reduces the construction and maintenance costs of the power transmission and distribution network.

[0004] However, it is found in the research that distributed power generation has randomness, volatility and intermittency, and after being connected to the power grid, it will cause problems such as reduced power grid stability and deteriorated power quality. Summary of the Invention

[0005] To solve the above-mentioned problems in the prior art, the present invention provides an optimization method and device for grid connection of distributed power sources.

[0006] In a first aspect, an embodiment of the present application provides an optimization method for grid connection of distributed power sources, including: determining the grid configuration parameters of a target area; wherein, the grid configuration parameters include the main transformer of the distribution network, system impedance, line impedance, grid distribution transformer and grid load; based on the grid configuration parameters of the target area, constructing a simplified distribution network model; wherein, the simplified distribution network model includes at least a plurality of divided nodes; determining a plurality of distributed power sources to be connected; substituting the simplified distribution network model and the plurality of distributed power sources into a particle swarm algorithm for iteration to determine the optimal solution of the objective function; wherein, the objective function aims to minimize network loss and voltage deviation; based on the optimal solution of the objective function, determining an optimal distribution network model; wherein, the optimal distribution network model includes the access nodes of the plurality of distributed power sources.

[0007] Optionally, the substituting the simplified distribution network model and the plurality of distributed power sources into a particle swarm algorithm for iteration to determine the optimal solution of the objective function includes: S1: calculating the numerical value of the objective function corresponding to the plurality of distributed power sources at the initial position based on the particle swarm algorithm; S2: updating the particles and calculating the numerical value of the objective function corresponding to the plurality of distributed power sources at the current position; repeating S1 to S2 until the optimal solution of the objective function is output after meeting the preset conditions.

[0008] Optionally, the preset condition includes: the value of the objective function corresponding to the multiple distributed power sources at the current position is less than a preset minimum threshold; correspondingly, the optimal solution of the output objective function is the value of the objective function corresponding to the multiple distributed power sources at the current position.

[0009] Optionally, the preset condition includes: the number of iterative updates of the particle has reached a preset iteration threshold; correspondingly, the optimal solution of the objective function is the minimum value among all the calculated values of the objective function.

[0010] Optionally, the calculation formula for the optimal solution of the objective function is: min[P loss , U p ; where min represents finding the minimum, P loss represents the network loss term; P li represents the active power loss of the i-th node after grid connection; n represents the total number of nodes; U p represents the voltage deviation term, U r represents the rated voltage value; U i represents the voltage value of the i-th node after grid connection.

[0011] Optionally, the particle swarm algorithm is a particle swarm optimization algorithm with random mutation.

[0012] Optionally, the determination of the multiple distributed power sources to be connected includes: for the simplified distribution network model, determining the access parameters of the distributed power sources; where the access parameters of the distributed power sources include the number, type, control mode, capacity, and initial position of the distributed power sources; based on the access parameters of the distributed power sources, determining the multiple distributed power sources to be connected.

[0013] Optionally, the types of the distributed power sources include: solar distributed power sources, wind energy distributed power sources, hydropower distributed power sources, biomass energy distributed power sources; the multiple distributed power sources to be connected determined include at least one type of the solar distributed power sources, wind energy distributed power sources, hydropower distributed power sources, and biomass energy distributed power sources.

[0014] Optionally, the objective function includes a network loss term, a voltage deviation term, and an economic cost term; the objective function aims to minimize the sum of the network loss term, the voltage deviation term, and the economic cost term.

[0015] Second aspect, the present application provides an optimization device for grid connection of distributed power sources, including: a first determination module, configured to determine grid configuration parameters of a target area; wherein, the grid configuration parameters include a main transformer of a distribution network, system impedance, line impedance, grid distribution transformers, and grid load; a construction module, configured to construct a simplified distribution network model based on the grid configuration parameters of the target area; wherein, the simplified distribution network model at least includes a plurality of divided nodes; a second determination module, configured to determine a plurality of distributed power sources to be connected; a calculation module, configured to substitute the simplified distribution network model and the plurality of distributed power sources into a particle swarm algorithm for iteration to determine an optimal solution of an objective function; wherein, the objective function aims to minimize network loss and voltage deviation; an optimization module, configured to determine an optimal distribution network model based on the optimal solution of the objective function; wherein, the optimal distribution network model includes connection nodes of the plurality of distributed power sources.

[0016] The beneficial effects of the present invention include: First, based on the grid configuration parameters of the target area itself, a simplified distribution network model is constructed. Then, a plurality of distributed power sources to be connected are determined. Subsequently, the two are combined, and the particle swarm algorithm is used to determine the optimal solution of the objective function, wherein the objective function aims to minimize network loss and voltage deviation. Finally, the optimal distribution network model can be determined by using the optimal solution of the objective function. The method provided by the embodiments of the present application takes the simplified nodes as reference points to determine the optimal connection positions of a plurality of distributed power sources, which can effectively reduce the network loss and voltage deviation after the grid connection of distributed power sources, and thus improve the grid stability and power quality after the grid connection. At the same time, by adopting the method of constructing a simplified distribution network model based on the grid configuration parameters of the target area itself, the calculation amount can be reduced, and a more rapid and simple calculation process can be provided. Description of the Drawings

[0017] Figure 1 It is a flowchart of the steps of an optimization method for grid connection of distributed power sources provided by an embodiment of the present invention;

[0018] Figure 2 It is a schematic diagram of a simplified distribution network model provided by an embodiment of the present invention;

[0019] Figure 3 It is a flowchart of the steps of another optimization method for grid connection of distributed power sources provided by an embodiment of the present invention;

[0020] Figure 4 It is a schematic diagram of the optimal distribution network model provided by an embodiment of the present invention;

[0021] Figure 5 It is a block diagram of modules of an optimization device for grid connection of distributed power sources provided by an embodiment of the present invention;

[0022] Figure 6 A block diagram of a module of an electronic device provided by an embodiment of the present invention. Detailed implementation manners

[0023] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0024] In addition, in the description of the specification and the appended claims of the present application, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0025] In the research, it is found that distributed power generation has randomness, volatility, and intermittency. After being connected to the power grid, it will cause problems such as reduced power grid stability and deteriorated power quality.

[0026] In view of the above problems, the present application proposes the following embodiments to solve the above technical problems.

[0027] Please refer to Figure 1 , an embodiment of the present application provides an optimization method for distributed power grid connection, including: step 101 to step 105.

[0028] Step 101: Determine the power grid configuration parameters of the target area.

[0029] Among them, the power grid configuration parameters include the main transformer of the distribution network, system impedance, line impedance, power grid distribution transformer, and power grid load.

[0030] Here, the main transformer of the distribution network is usually installed in the substation and is a large transformer used for voltage boosting and reducing. Its main function is to boost the electric energy output by the generator to a higher voltage level for long-distance power transmission, or to reduce the transmission voltage to a low voltage level suitable for local power distribution.

[0031] The system impedance refers to the combination of the total resistance and reactance of all electrical equipment and lines in the entire power system.

[0032] The line impedance refers to the resistance and reactance of the transmission line itself.

[0033] The power grid distribution, that is, the distribution transformer in the power grid. It is used to further reduce the electric energy output by the substation to the voltage level required by users.

[0034] The power grid load refers to the power demand of equipment or users that consume energy in the power system.

[0035] That is, in order to realize the grid connection of distributed power sources in the target area, it is first necessary to determine the grid configuration parameters of the target area.

[0036] The above-mentioned target area can refer to towns and so on, which is not limited in this application.

[0037] Step 102: Construct a simplified distribution network model based on the grid configuration parameters of the target area.

[0038] Among them, the simplified distribution network model at least includes multiple divided nodes.

[0039] That is, according to the grid configuration parameters of the target area, the power system of the entire target area is simplified to generate a simplified distribution network model.

[0040] Step 103: Determine multiple distributed power sources to be connected.

[0041] Step 104: Substitute the simplified distribution network model and multiple distributed power sources into the particle swarm optimization algorithm for iteration to determine the optimal solution of the objective function.

[0042] Among them, the objective function aims to minimize network loss and voltage deviation.

[0043] That is, the embodiment of this application proposes to solve the model based on the particle swarm optimization algorithm, aiming to minimize network loss and voltage deviation, and transform the non-linear quadratic programming problem into a traditional quadratic linear programming problem.

[0044] Step 105: Determine the optimal distribution network model based on the optimal solution of the objective function.

[0045] Among them, the optimal distribution network model includes the access nodes of multiple distributed power sources. Finally, based on the access nodes of each distributed power source in the optimal distribution network model, the access positions of the distributed power sources in the target area can be determined.

[0046] In summary, the optimization method for distributed power grid connection provided by the embodiments of the present application has the following beneficial effects: First, based on the grid configuration parameters of the target area itself, a simplified distribution network model is constructed. Then, multiple distributed power sources to be connected are determined. Subsequently, the two are combined, and the particle swarm optimization algorithm is used to determine the optimal solution of the objective function, where the objective function aims to minimize network loss and voltage deviation. Finally, the optimal distribution network model can be determined using the optimal solution of the objective function. The method provided by the embodiments of the present application takes the simplified nodes as reference points to determine the optimal connection positions of multiple distributed power sources, which can effectively reduce the network loss and voltage deviation after the distributed power sources are connected to the grid, thereby improving the stability of the grid and the power quality after grid connection. At the same time, by using the method of constructing a simplified distribution network model based on the grid configuration parameters of the target area itself, the amount of calculation can be reduced, and a faster and more convenient calculation process can be provided.

[0047] Optionally, determining multiple distributed power sources to be connected includes: for the simplified distribution network model, determining the connection parameters of the distributed power sources; where the connection parameters of the distributed power sources include the number, type, control method, capacity, and initial position of the distributed power sources; based on the connection parameters of the distributed power sources, multiple distributed power sources to be connected are determined.

[0048] Here, the number of distributed power sources can refer to the total number planned in the power system, and this number can depend on power demand, available space, economic considerations, etc. That is, the specific data of the distributed power sources can be set according to the actual situation.

[0049] The types of distributed power sources include: solar distributed power sources, wind energy distributed power sources, hydraulic distributed power sources, and biomass energy distributed power sources.

[0050] Among them, a solar distributed power source can refer to converting solar energy into electrical energy using solar photovoltaic panels. A wind energy distributed power source can refer to converting wind energy into electrical energy using wind turbines. A hydraulic distributed power source can refer to converting water flow energy into electrical energy using hydraulic turbines. A biomass energy distributed power source can refer to generating electricity using biomass fuels (such as wood, straw, etc.).

[0051] Finally, the multiple distributed power sources to be connected determined include at least one type of the solar distributed power source, wind energy distributed power source, hydraulic distributed power source, and biomass energy distributed power source.

[0052] The control method of the distributed power source can refer to demand response control, frequency response control, etc.

[0053] The capacity of the distributed power source usually determines its maximum output power or the maximum electrical energy it can provide.

[0054] The initial positions of distributed power sources correspond to the nodes in the simplified distribution network model, which can be set by users or randomly set.

[0055] It can be seen that in the embodiments of the present application, various combination methods of distributed power sources are comprehensively considered, and it is not limited to the grid connection optimization of a single type of distributed power source. That is, it can be applied to the grid connection optimization of various distributed power sources defined by users, so that the distributed power sources finally connected to the target area can have different types, control methods, capacities, etc.

[0056] Optionally, substituting the simplified distribution network model and multiple distributed power sources into the particle swarm algorithm for iteration to determine the optimal solution of the objective function may specifically include: S1: Based on the particle swarm algorithm, calculate the values of the objective function corresponding to multiple distributed power sources at the initial position; S2: Update the particles and calculate the values of the objective function corresponding to multiple distributed power sources at the current position; Repeat S1 to S2 until the optimal solution of the objective function is output after meeting the preset conditions.

[0057] It should be noted that the particle swarm algorithm is an optimization algorithm that simulates the foraging behavior of bird flocks. Here, by combining the particle swarm algorithm to obtain the optimal solution of the objective function, and then finding the best positions of distributed power sources, the network loss and voltage deviation after the grid connection of distributed power sources can be effectively reduced, and the stability and power quality of the power grid after the grid connection can be improved.

[0058] Optionally, the preset conditions may include: the values of the objective function corresponding to multiple distributed power sources at the current position are less than a preset minimum threshold.

[0059] Among them, the preset minimum threshold is a set threshold for ensuring the optimization degree, which can be set according to the actual situation.

[0060] Correspondingly, the optimal solution of the output objective function is the value of the objective function corresponding to multiple distributed power sources at the current position.

[0061] It can be understood that in the process of iteration using the particle swarm algorithm, the cut-off condition is that the value of the objective function is less than the preset minimum threshold, so as to reasonably control the optimization degree. And since a sufficiently optimized solution is found, the algorithm will no longer continue to search, which can also save computing power and resources to a certain extent.

[0062] Optionally, the preset conditions may include: the number of iterative updates of the particles has reached a preset iteration threshold.

[0063] The preset iteration threshold can also be set according to the actual situation, such as 100 times, 500 times, etc. Usually, the preset iteration threshold can be set to a larger value to achieve global search.

[0064] Correspondingly, the optimal solution of the objective function is the minimum value among all the calculated values of the objective function.

[0065] It can be understood that in the process of using the particle swarm optimization algorithm for iteration, taking the iteration update times reaching the preset iteration threshold as the cut-off condition can ensure the integrity of the algorithm search process, that is, after a comprehensive search, the global optimal solution is output.

[0066] Optionally, the calculation formula for the optimal solution of the objective function is: min[P loss , U p .

[0067] Among them, min represents finding the minimum, and P loss represents the network loss term; P li represents the active power loss of the i-th node after grid connection; n represents the total number of nodes; U p represents the voltage deviation term, U r represents the rated voltage value; U i represents the voltage value of the i-th node after grid connection.

[0068] Among them, there are also equality constraints and inequality constraints set in the algorithm, including: g(x) = 0, l(x) ≤ 0; where, g(x) represents the equality constraint; l(x) represents the inequality constraint; x represents the access parameters of the distributed power source, which can include at least one of the type, control method, capacity, and location of the distributed power source.

[0069] Optionally, the particle swarm algorithm is a particle swarm optimization algorithm with random mutation.

[0070] It should be noted that the optimization method for distributed power source grid connection provided in this embodiment introduces a particle swarm optimization algorithm with random mutation, which can help the particle swarm jump out of the local optimal solution, thereby increasing the possibility of finding the global optimal solution. That is, it helps to more effectively handle complex multi-objective optimization problems (including network loss and voltage deviation) in the process of distributed power source grid connection optimization.

[0071] Optionally, the objective function includes a network loss term, a voltage deviation term, and an economic cost term.

[0072] The objective function aims to minimize the sum of the network loss term, the voltage deviation term, and the economic cost term.

[0073] Among them, the expression of the objective function can be:

[0074] f(x) = ω 1 *P loss + ω 2 *U p + ω3 *Q;

[0075] Among them, f(x) represents the objective function; ω 1 represents the weight of the network loss term; ω 2 represents the weight of the voltage deviation term; ω 3 represents the weight of the economic cost term; Q represents the economic cost term, which can be determined by parameters such as the type, control method, capacity, and location of the distributed power source; x represents the access parameters of the distributed power source, which can include at least one of the type, control method, capacity, and location of the distributed power source.

[0076] It can be seen that the embodiments of the present application comprehensively consider the network loss, voltage deviation, and economic cost after the distributed power source is connected to the grid, and combine the particle swarm algorithm to solve the multi-objective optimization problem, so as to realize the grid connection method that minimizes the network loss and voltage deviation while considering the economy of the system after grid connection.

[0077] Next, a specific example is used to illustrate the optimization method for distributed power source grid connection provided by the embodiments of the present application.

[0078] First, determine the grid configuration parameters of the target area, which include the main transformer of the distribution network, system impedance, line impedance, grid distribution transformer, and grid load.

[0079] Then, based on the grid configuration parameters of the target area, construct a simplified distribution network model. The simplified distribution network model can refer to Figure 2 , where S N represents the equivalent power source, U N represents the rated value of the root node voltage, R 1 , R 2 , R 3 correspond to the line resistances respectively, X 1 , X 2 , X 3 correspond to the line reactances respectively, DG 1 , DG 2 represent two different distributed power sources, and n represents the number of nodes.

[0080] It should be noted that Figure 2 is only a simplified schematic diagram, and the content of the other nodes is omitted.

[0081] Then, determine multiple distributed power sources to be connected, and combine the particle swarm algorithm to iteratively obtain the optimal solution of the objective function. The specific process can refer to Figure 3, including: initializing parameters, which may include setting the maximum population size and the number of iterations, then calculating the objective function value, and then updating the particles, determining whether the accuracy requirement (corresponding to the preset condition in the foregoing embodiment) is met. If not, continue with iterative updates. If so, output the optimal objective function and the optimal configuration scheme of distributed power sources.

[0082] It should be noted that the objective function provided in the embodiments of the present application can be constructed based on the analysis of the processing characteristics of solar power generation. At the same time, the embodiments of the present application also consider the analysis of the voltage fluctuations of the distribution network connected to the distributed power source. The voltage of the distribution network is related to the network power flow distribution. When the active power or load of the distribution network changes, it will cause fluctuations in each node of the distribution network.

[0083] First, analyze the output characteristics of solar energy. The expression for the active output of a distributed solar panel is:

[0084]

[0085] Among them, P st represents the active output of the solar panel; P N represents the rated power of the solar panel; I N represents the rated current of the solar panel, T N represents the rated light temperature of the photovoltaic panel; α T represents the temperature coefficient T t is the light temperature value of the battery panel at time t, r t represents the light intensity value at time t.

[0086] The solar output shows a linear relationship with the light intensity, and the light intensity follows a beta distribution. Therefore, the solar active output also follows a beta distribution, as follows:

[0087]

[0088] Among them, f(P st ) represents the beta distribution corresponding to P st ; P stmax represents the maximum value of the photovoltaic output; α and β represent two parameters in, and r is used to distinguish different shape parameters.

[0089] Secondly, consider the analysis of the voltage fluctuations of the distribution network with DG access. The voltage of the distribution network is related to the network power flow distribution. When the active power or load of the distribution network changes, it will cause fluctuations in each node of the distribution network.

[0090] Assume that the load power of distribution network node k is: P lk +jQ lk ; P lk represents the active power of distribution network node k; Qlk Denote the reactive power of distribution network node k; j represents the imaginary unit (the subsequent parameter j is the same).

[0091] The power of the distributed power source connected to the grid is divided into P dk +jQ dk ; P dk Denote the active power of the distributed power source; Q dk Denote the reactive power of the distributed power source;

[0092] The impedance magnitude of feeder k is R k +jX k ; R k Denote the resistance of feeder k; X k Denote the reactance of feeder k;

[0093] The rated value of the distribution transformer capacity at node k is S NTk ;

[0094] Among them, the proportions of active and reactive power losses are α k , β k ;

[0095] The power factors of the distribution network system load and the distributed power source are

[0096] The rated value of the root node voltage is U N ;

[0097] The magnitudes of the total distribution network load power and the total distributed power source power are as follows:

[0098]

[0099] Among them, P l Denote the total distribution network load power; Q l Denote the total distribution network load power; denote the total reactive power of the distribution network; Q d Denote the total reactive power of the distributed power source; S NT Denote the total rated value of the distribution transformer capacity; P d Denote the total active power of the distributed power source.

[0100] The voltage deviation calculation caused by the distributed power source connected to the grid at position k is as follows:

[0101]

[0102] Among them, R i Denote the resistance corresponding to feeder i, X i Denote the reactance corresponding to feeder i;

[0103] The magnitude of the voltage fluctuation caused at node k is as follows:

[0104]

[0105] Finally, taking the IEEE 33-node distribution system as an example, the simulation calculation of the distributed generation optimal configuration model is carried out. The base voltage at the network head end can be 12.66 kV, the three-phase power base value can be 10 MVA, and the load power can be 4369 kVA, where the active power is 3715 kW and the reactive power is 2300 kvar. The optimal configuration of DG grid connection is calculated using the particle swarm optimization algorithm based on random mutation. The final optimal distribution network model is as Figure 4 shown. According to the simulation results, the effectiveness and feasibility of the distributed generation optimal configuration model proposed in this application are proved. After the distributed energy is optimally configured and incorporated into the distribution network system, it can effectively reduce the network loss and improve the power quality of the system at the same time.

[0106] Please refer to Figure 5 , based on the same inventive concept, this application provides an optimization device 500 for distributed generation grid connection, including: a first determination module 501 for determining the grid configuration parameters of the target area; wherein, the grid configuration parameters include the main transformer of the distribution network, system impedance, line impedance, grid distribution transformer and grid load; a construction module 502 for constructing a simplified distribution network model based on the grid configuration parameters of the target area; wherein, the simplified distribution network model includes at least a plurality of divided nodes; a second determination module 503 for determining a plurality of distributed generations to be connected; a calculation module 504 for substituting the simplified distribution network model and the plurality of distributed generations into the particle swarm algorithm for iteration to determine the optimal solution of the objective function; wherein, the objective function aims to minimize the network loss and voltage deviation; an optimization module 505 for determining the optimal distribution network model based on the optimal solution of the objective function; wherein, the optimal distribution network model includes the access nodes of the plurality of distributed generations.

[0107] Please refer to Figure 6 , based on the same inventive concept, an embodiment of this application provides a module frame of an electronic device 600 applying the above method. The electronic device 600 includes: at least one processor 601 ( Figure 6 only one is shown in the figure), a memory 602, and a computer program 603 stored in the memory 602 and executable on at least one processor 601. When the processor 601 executes the computer program 603, the steps of the method in any of the foregoing embodiments are implemented.

[0108] The electronic device 600 can be a server, a personal computer, a laptop computer, and so on.

[0109] Those skilled in the art can understand, Figure 6The electronic device 600 is merely an example and does not limit the electronic device 600. It may include more or fewer components than those shown, or combine certain components, or have different components.

[0110] The so-called processor 601 may be a central processing unit (CPU), and the processor 601 may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0111] In some embodiments, the memory 602 may be an internal storage unit of the electronic device 600, such as the hard disk or memory of the electronic device 600. In other embodiments, the memory 602 may also be an external storage device of the electronic device 600, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 600. Further, the memory 602 may also include both the internal storage unit and the external storage device of the electronic device 600.

[0112] It should be noted that for the above-mentioned systems, devices, etc., since they are based on the same concept as the method embodiments of the present application, the modules designed by the systems, and the steps and technical effects performed by the devices can be referred to the method embodiment section, and will not be elaborated here.

[0113] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiments can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of this application. The specific working processes of the units and modules in the above system can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0114] An embodiment of this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the foregoing method embodiments can be implemented.

[0115] An embodiment of this application provides a computer program product. When the computer program product runs on a mobile terminal, the mobile terminal can implement the steps in the foregoing method embodiments when executed.

[0116] If the above integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above method embodiments of this application, a computer program can be used to instruct relevant hardware to complete. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps in the foregoing method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the photographing device / electronic device, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disc, etc.

[0117] In the above embodiments, the descriptions of the various embodiments have their own emphases. For parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0118] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0119] In the embodiments provided in this application, it should be understood that the disclosed device / network device and method can be implemented in other ways. For example, the device / network device embodiments described above are merely illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical, mechanical or other form.

[0120] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0121] The above-described embodiments are only used to illustrate the technical solutions of this application and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application and should all be included within the protection scope of this application.

Claims

1. A distributed power grid connection optimization method, characterized in that: include: Determine the grid configuration parameters of the target area; wherein the grid configuration parameters include the main transformer of the distribution network, system impedance, line impedance, grid distribution transformer and grid load; Based on the power grid configuration parameters of the target area, a simplified distribution network model is constructed; wherein the simplified distribution network model includes at least a plurality of divided nodes; Determine multiple distributed power sources to be connected; Substituting the simplified distribution network model and the multiple distributed power sources into the particle swarm algorithm for iteration to determine the optimal solution of the objective function; wherein the objective function aims to minimize network loss and voltage deviation; Based on the optimal solution of the objective function, an optimal distribution network model is determined; wherein the optimal distribution network model includes access nodes of the multiple distributed power sources.

2. The distributed power grid connection optimization method according to claim 1, characterized in that: Substituting the simplified distribution network model and the multiple distributed power sources into a particle swarm algorithm for iteration to determine an optimal solution of an objective function includes: S1: Based on the particle swarm algorithm, calculate the values ​​of the objective functions corresponding to the multiple distributed power sources at the initial positions; S2: updating the particles and calculating the values ​​of the objective functions corresponding to the multiple distributed power sources at the current positions; Repeat S1 to S2 until the optimal solution of the objective function is output after the preset conditions are met.

3. The method for optimizing the grid connection of distributed power sources according to claim 2, characterized in that: The preset conditions include: The values ​​of the objective functions corresponding to the multiple distributed power sources at the current position are less than a preset minimum threshold; Correspondingly, the optimal solution of the output objective function is the value of the objective function corresponding to the multiple distributed power sources at the current position.

4. The method for optimizing the grid connection of distributed power sources according to claim 2, characterized in that: The preset conditions include: The number of iterative updates to the particles has reached the preset iteration threshold; Correspondingly, the optimal solution of the objective function is the minimum value of all the calculated objective function values.

5. The method for optimizing the grid connection of distributed power sources according to claim 1, characterized in that: The calculation formula for the optimal solution of the objective function is: min[P loss ,IN p ]; Among them, min means to find the minimum, P loss represents the network loss term; P li represents the active power loss of the ith node after grid connection; n represents the total number of nodes; U p represents the voltage offset term, U r Indicates the rated voltage value; U i Represents the voltage value of the th node after grid connection.

6. The method for optimizing the grid connection of distributed power sources according to claim 1, characterized in that: The particle swarm algorithm is a particle swarm optimization algorithm with random mutation.

7. The method for optimizing the grid connection of distributed power sources according to claim 1, characterized in that: The step of determining a plurality of distributed power sources to be connected includes: Determine the access parameters of the distributed power source for the simplified distribution network model; wherein the access parameters of the distributed power source include the number, type, control method, capacity and initial position of the distributed power source; Based on the access parameters of the distributed power sources, the plurality of distributed power sources to be accessed are determined.

8. The method for optimizing the grid connection of distributed power sources according to claim 2, characterized in that: The types of distributed power sources include: solar distributed power sources, wind distributed power sources, hydro distributed power sources, and biomass distributed power sources; The determined multiple distributed power sources to be connected include at least one type of solar distributed power source, wind distributed power source, hydro distributed power source, and biomass distributed power source.

9. The distributed power grid connection optimization method according to claim 1, characterized in that: The objective function includes a network loss term, a voltage deviation term and an economic cost term; The objective function aims to minimize the sum of the network loss term, the voltage deviation term and the economic cost term.

10. An optimization device for distributed power grid connection, characterized in that: include: A first determination module is used to determine the grid configuration parameters of the target area; wherein the grid configuration parameters include the main transformer of the distribution network, system impedance, line impedance, grid distribution transformer and grid load; A construction module, used to construct a simplified distribution network model based on the power grid configuration parameters of the target area; wherein the simplified distribution network model includes at least a plurality of divided nodes; A second determination module is used to determine a plurality of distributed power sources to be connected; A calculation module, used for substituting the simplified distribution network model and the multiple distributed power sources into a particle swarm algorithm for iteration to determine an optimal solution of an objective function; wherein the objective function aims to minimize network loss and voltage deviation; An optimization module is used to determine an optimal distribution network model based on an optimal solution of the objective function; wherein the optimal distribution network model includes access nodes of the multiple distributed power sources.

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